Introduction 

Large Language Models (LLMs) are considered to be an AI revolution, altering how users interact with technology and the world around us. Especially with deep learning algorithms in the picture data, professionals can now train huge datasets that will be able to recognize, summarize, translate, predict, and generate text and other types of content.

As LLMs become an increasingly important part of our digital lives, advancements in natural language processing (NLP) applications such as translation, chatbots, and AI assistants are revolutionizing the healthcare, software development, and financial industries.

Therefore, to get a closer view of the challenges, we will discuss the four limitations of LLMs devise a decision to eliminate those limitations, and focus on the benefits of LLMs. 

1. Limitations of LLMs in the Digital World

We know that LLMs are impressive technology, but they are not without flaws. Users often face issues such as contextual understanding, generating misinformation, ethical concerns, and bias. These limitations not only challenge the fundamentals of natural language processing and machine learning but also recall the broader concerns in the field of AI. Therefore, addressing these constraints is critical for the secure and efficient use of LLMs. 

Let’s look at some of the limitations:

1.1. Contextual Understanding

LLMs are conditioned on vast amounts of data and can generate human-like text, but they sometimes struggle to understand the context. While humans can link with previous sentences or read between the lines, these models battle to differentiate between any two similar word meanings to truly understand a context like that. For instance, the word “bark” has two different meanings; one “bark” refers to the sound a dog makes, whereas the other “bark” refers to the outer covering of a tree. If the model isn’t trained properly, it will provide incorrect or absurd responses, creating misinformation.

1.2. Misinformation 

Even though LLM’s primary objective is to create phrases that feel genuine to humans; however, at times these phrases are not necessarily to be truthful. LLMs generate responses based on their training data, which can sometimes create incorrect or misleading information. It was discovered that LLMs such as ChatGPT or Gemini often “hallucinate” and provide convincing text that contains false information, and the problematic part is that these models point their responses with full confidence, making it hard for users to distinguish between fact and fiction.

1.3. Ethical Considerations 

There are also ethical concerns related to the use of LLMs. These models often generate intricate information, but the source of the information remains unknown, hence questioning its transparency in its decision-making processes. To add to it, there is less clarity on the source of these datasets when trained, leading to creating deep fake content or generating misleading news.

1.4. Potential Bias

As LLMs are conditioned to use large volumes of texts from diverse sources, they also carry certain geographical and societal biases within their models. While data professionals have been rigorously working to keep the systems diplomatic, however, it has been observed that LLM-driven chatbots tend to be biased toward specific ethnicities, genders, and beliefs.

2. Addressing the Constraints of LLMs

Now that we have comprehended the limitations that LLMs bring along, let us peek at particular ways that we can manage them:

2.1. Carefully Evaluate  

As LLMs can generate harmful content, it is best to rigorously and carefully evaluate each dataset. We believe human review could be one of the safest options when it comes to evaluation, as it is judged based on a high level of knowledge, experience, and justification. However, data professionals can also opt for automated metrics that can be used to assess the performance of LLM models. Further, these models can also be put through negative testing methods, which break down the model by experimenting with misleading inputs; this method helps to pinpoint the model’s weaknesses.

2.2. Formulating Effective Prompts 

The way users phrase the prompts, the LLMs provide results, but with the help of a well-designed prompt, they can make huge differences and provide accuracy and usefulness while searching for answers. Data professionals can opt for techniques such as prompt engineering, prompt-based learning, and prompt-based fine-tuning to interact with these models.

2.3. Improving Transparency and Removing Bias

It might be a difficult task for data professionals to understand why LLMs make specific predictions, which leads to bias and fake information. However, there are tools and techniques available to enhance the transparency of these models, making their decisions more interpretable and responsible. Looking at the current scenario, IT researchers are also exploring new strategies for differential privacy and fairness-aware machine learning to address the problem of bias.

Final Thoughts

LLMs have been transforming the landscape of NLP by offering exceptional capabilities in interpreting and generating human-like text. Yet, there are a few hurdles, such as model bias, lack of transparency, and difficulty in understanding the output, that need to be addressed immediately. Fortunately, with the help of a few strategies and techniques, such as using adversarial text prompts or implementing Explainable AI, data professionals can overcome these limitations. 

To sum up, LLMs might come with a few limitations but have a promising future. In due course of time, we can expect these models to be more reliable, transparent, and useful, further opening new doors to explore this technological marvel.

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In a shocking revelation, a massive data leak has exposed sensitive personal information of over 1.6 million individuals, including Indian military personnel, police officers, teachers, and railway workers. This breach, discovered by cybersecurity researcher Jeremiah Fowler, included biometric data, birth certificates, and employment records and was linked to the Hyderabad-based companies ThoughtGreen Technologies and Timing Technologies. 

While this occurrence is painful, it is far from shocking. 

The database, containing 496.4 GB of unprotected data, was reportedly found to be available on a dark web-related Telegram group. The exposed information included facial scans, fingerprints, identifying marks such as tattoos or scars, and personal identification documents, underscoring a growing concern about the security protocols of private contractors who manage sensitive government data.

The impact of such breaches goes far beyond what was capable years ago. In the past, stolen identity would have led to the opening of fake credit cards or other relatively containable incidents. Today, a stolen identity that includes biometric data or an image with personal information is enough for threat actors to create a deep fake and sow confusion amongst personal and professional colleagues. This allows unauthorised personnel to gain access to classified information from private businesses and government agencies, posing a significant risk to national security.

Deepfakes even spread fear throughout southeast Asia, specifically during India’s recent Lok Sabha, during which 75% of potential voters reported being exposed to the deceitful tool. 

The Risks of Outsourcing Cybersecurity

Governments increasingly rely on private contractors to manage and store vast amounts of sensitive data. However, this reliance comes with significant risks. Private firms often lack the robust cybersecurity measures that government systems can implement. 

However, with India continuing to grow as a digital and cybersecurity powerhouse, the hope was that outsourcing the work would save taxpayers money while providing the most advanced technology possible. 

However, a breach risks infecting popular software or other malicious actions such as those seen in other supply chain attacks, which are a stark reminder of the need for stringent security measures and regular audits of third-party vendors.

Leveraging AI for Cybersecurity

Cybercrime is on the rise globally, with threat actors becoming more sophisticated in their methods. The growth of AI has further complicated the cybersecurity landscape. While AI offers powerful tools for defence, it also provides new capabilities for cybercriminals who can use it to pry and prod at a system faster than ever before until a vulnerability is found. What’s more, this technology can be used to automate attacks, create more convincing phishing schemes, and even develop malware that can adapt and evolve to avoid detection.

While this may sound like the ultimate nightmare scenario, this same technology offers significant advantages to cybersecurity teams. AI-driven tools can automate threat detection and response, reducing the burden on human analysts and allowing them to focus on more complex tasks. For instance, large language models (LLMs) can process and analyse vast amounts of data quickly, identifying threats in real-time and providing actionable insights.

AI can also play a crucial role in upskilling employees within cybersecurity teams. With the implementation of LLMs, even less experienced team members can make impactful decisions based on AI-driven insights. These models allow analysts to use natural language queries to gather information, eliminating the need for specialised training in various querying languages. By running queries like “Can vulnerability ‘#123’ be found anywhere in the network?” teams can quickly identify potential threats and take appropriate action.

Furthermore, AI assists in automating routine tasks, allowing cybersecurity professionals to focus on strategic initiatives. It can offer next-step recommendations based on previous actions, enhancing the decision-making process. For example, when an alert is triggered, AI can provide insights such as “This alert is typically dismissed by 90% of users” or “An event looks suspicious. Click here to investigate further.” 

This streamlines operations and accelerates the learning curve for junior analysts, allowing them to quickly become proficient in identifying and mitigating threats, thus leveling up the entire team’s capabilities.

Balancing the Scales

As it has always been in the battle between cybersecurity teams and threat actors, there is no one-size-fits-all solution that can secure all networks. However, machine-speed attacks need a machine-speed autonomous response that only AI can deliver. 

The recent data leak in India highlights the importance of robust cybersecurity measures, especially when dealing with sensitive government data. As cyber threats evolve, so too must our defences. By leveraging the power of AI, cybersecurity teams can remain one step ahead on the frontlines of protecting government data, digital economies, and even the complex infrastructure that keeps society functioning as it does.

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Joscha, would you mind sharing with us some insights into your professional journey and how you arrived at your current role as Head of Product at Connectly.ai?

My path to the tech industry and product management took a bit of an unconventional route. My introduction to product development started in the hair care sector, where I had the opportunity to dive deep into human needs and master the art of user-centric design. When I found myself looking for a more dynamic environment, I embarked on a nearly decade-long journey at Google.

I began in sales and gained invaluable insights into customer pain points and the intricacies of building relationships. This then laid the groundwork for my transition into a product role within the Ads organization at Google.

After my time at Google, I took a leap into the unknown and joined Connectly as the fourth employee—a decision fueled by the thrill of building something from the ground up.. Today, we have a global team of more than 50, we partner with category-defining customers, and we are pushing the boundaries of AI research and product development. I couldn’t be more excited about where we’re headed next.

How does RAG revolutionize customer interaction and business intelligence in sales, with a special emphasis on the critical aspects of accuracy and timeliness of information?

By combining a generative model with a retrieval system, Retrieval-Augmented Generation (RAG) enhances AI responses with accurate, current data. 

Large Language Models (LLMs) in a production environment are constrained by their static datasets, and often lack in accuracy and timeliness. However, RAG introduces a dynamic component that leverages real-time external databases. This ensures that every piece of information it provides or action it recommends is grounded in the latest available data.

As the Head of Product at Connectly.ai, how do you foresee integrating RAG technology into your product offerings to enhance customer experiences and sales effectiveness?

RAG is one part of a cohesive AI strategy. At Connectly we also found that we had to start training our own embeddings as well as models to help make our AI Sales Assistant efficient, fast and reliable.

Traditional AI models often encounter challenges with stale data sets and complex queries. How does RAG address these limitations, and what advantages does it bring to AI systems in terms of improving responsiveness and relevance of information?

Complex queries that would stump earlier AI models are now within reach with enhanced query resolution. By employing sophisticated retrieval systems to gather data from numerous sources, RAG can dissect and respond to multifaceted questions in a nuanced way that was previously unachieveable. 

Additionally, RAG has the capability to pull in and analyze data from diverse sources in real-time, which transforms it into a powerful tool for market analysis. This can then equip businesses and leaders with the agility to adapt to market shifts with insights derived from the most current data, offering a hard-to-match competitive edge.

Could you kindly elaborate on how Connectly.ai is leveraging RAG to enhance its AI sales assistants and provide more personalized and contextually relevant interactions for users?

Of course! RAG is one part of the AI sales assistant that we have built. Businesses share their product catalog with Connectly to inform our sales assistant. This product catalog can have many million products with different variants. The inventory and prices might change on a daily basis. In order to provide the end customer with real time and reliable data, we leverage RAG as part of our architecture.

In your esteemed experience, what key considerations or best practices should companies keep in mind when seeking to enhance their AI models with technologies like RAG to create better customer experiences?

I would recommend starting with a narrow use case first and learn from there. In our case we had to learn the hard way that, for example, offering a multi language product from the start came with many hurdles. Clothes sizing for example can be different from country to country. English makes up more than 40% of common crawl data, so language embeddings and foundational models will work better in English first.  

What personal strategies or approaches do you employ to stay informed about emerging technologies and industry trends, particularly in the realm of AI and customer interaction?

There is so much happening and the AI industry is moving at a crazy pace. I have gathered a list of people I follow on X to stay up to date with some of the latest trends and discussions. I’m also lucky to be living in San Francisco where you will overhear a conversation about AI just about anywhere you go. 

Drawing from your expertise, what valuable advice would you extend to our readers who are interested in implementing RAG or similar technologies to improve their own AI systems and customer interactions?

If you are incorporating AI into your business, I would always start with a design partner in mind who can provide you valuable feedback and insights and is willing to build with you. This can be an external stakeholder like a customer or an internal team. The external validation is extremely helpful and important to help solve actual problems and pain points. 

As we come to the end of our discussion, would you be open to sharing any final thoughts or insights regarding the future of RAG technology and its implications for sales and customer engagement?

There is a lot of interesting discussion around the future of memory in AI. If a sales assistant can remember and learn from all previous conversations it had with a customer, it will evolve into a true personal shopper. 

Finally, Joscha, could you provide us with some insight into what’s next for Connectly.ai and how RAG fits into your broader product roadmap for enhancing customer experiences?

We have a lot of exciting launches in the pipeline. We launched our sales assistant, Sofia AI, about 6 months ago and are already partnering with major global brands. One of the new features I am most excited about is our continued work on AI insights from the conversations customers are having with our sales assistant. These insights can be imported directly into a CRM and help our businesses truly understand their customers. Previously this would have only been possible by interviewing every member in the Sales staff.

Joscha Koepke

Head of Product at Connectly.ai

Joscha Koepke is Head of Product at Connectly. As part of the company’s founding team, he leads the product team in building and innovating its AI-powered conversational commerce platform, which enables businesses to operate the full flywheel – marketing, sales, transactions, customer experience – all within the customer’s thread of choice. Prior to Connectly, Joscha was a Global Product Lead for Google, leading the product & go-to-market strategy of emerging online-to-offline ad format products across Search, Display, YouTube, & Google Maps. 

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Background:

Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

Code optimization in AI is all about maximizing results while minimizing inefficiencies and operational costs. It’s a key factor in driving the success and sustainability of AI initiatives in the dynamic and competitive landscape of modern businesses.

Code Optimization:

What are some common challenges and issues businesses face with code optimization when implementing AI solutions?

Businesses implementing AI solutions often encounter several challenges with code optimization, mainly due to the dynamic and complex nature of AI systems compared to traditional software optimization. Achieving optimal AI performance requires a delicate balance between code, model, and data, making the process intricate and multifaceted. This complexity is compounded by the need for continuous adaptation of AI systems, as they require constant updating to stay relevant and effective in changing environments.

A significant challenge is the scarcity of skilled performance engineers, who are both rare and expensive. In cities like London, costs can reach up to £500k per year, making expertise a luxury for many smaller companies.

Furthermore, the optimization process is time- and effort-intensive, particularly in large codebases. It involves an iterative cycle of fine-tuning and analysis, demanding considerable time even for experienced engineers. Large codebases amplify this challenge, requiring significant manpower and extended time frames for new teams to contribute effectively.

These challenges highlight the necessity for better tools to make code optimization more accessible and manageable for a wider range of businesses.

Could you share some examples of the tangible benefits businesses can achieve through effective code optimization in AI applications?

AI applications are subject to change along three axes: model, code, and data. At TurinTech, our evoML platform enables users to generate and optimize efficient ML code. Meanwhile, our GenAI-powered code optimization platform, Artemis AI, can optimize more generic application code. Together, these two products help businesses significantly enhance cost-efficiency in AI applications.

At the model level, different frameworks or libraries can be used to improve model efficiency without sacrificing accuracy. However, transitioning an ML model to a different format is complex and typically requires manual conversion by developers who are experts in these frameworks.

At TurinTech AI, we provide advanced functionalities for converting existing ML models into more efficient frameworks or libraries, resulting in substantial cost savings when deploying AI pipelines.

One of our competitive advantages is our ability to optimize both the model code and the application code. Inefficient code execution, which consumes excess memory, energy, and time, can be a hidden cost in deploying AI systems. Code optimization, often overlooked, is crucial for creating high-quality, efficient codebases. Our automated code optimization features can identify and optimize the most resource-intensive lines of code, thereby reducing the costs of executing AI applications.

Our research at TurinTech has shown that code optimization can improve the execution time of specific ML codebases by up to around 20%. When this optimized code was tested in an Azure-based cloud environment, we observed cost savings of about 30% per hour for the virtual machine size used. This highlights the significant impact of optimizing both the model and code levels in AI applications.

Are there any best practices or strategies that you recommend for businesses to improve their code optimization processes in AI development?

Code optimization leads to more efficient, greener, and cost-effective AI. Without proper optimization, AI can become expensive and challenging to scale.

Before embarking on code optimization, it’s crucial to align the process with your business objectives. This alignment involves translating your main goals into tangible performance metrics, such as reduced inference time and lower carbon emissions.

Empowering AI developers with advanced tools can automate and streamline the code optimization process, transforming what can be a lengthy and complex task into a more manageable one. This enables developers to focus on more innovative tasks.

In AI development, staying updated with AI technologies and trends is crucial, particularly by adopting a modular tech stack. This approach not only ensures efficient code optimization but also prepares AI systems for future technological advancements.

Finally, adopting eco-friendly optimization practices is more than a cost-saving measure; it’s a commitment to sustainability. Efficient code not only reduces operational costs but also lessens the environmental impact. By focusing on greener AI, businesses can contribute to a more sustainable future while reaping the benefits of efficient code.

Generative AI and Its Impact:

Generative AI has been a hot topic in the industry. Could you explain what generative AI is and how it’s affecting businesses and technology development?

Generative AI, a branch of artificial intelligence, excels in creating new content, such as text, images, code, video, and music, by learning from existing datasets and recognizing patterns.

Its swift adoption is ushering in a transformative era for businesses and technology development. McKinsey’s research underscores the significant economic potential of Generative AI, estimating it could contribute up to $4.4 trillion annually to the global economy, primarily through productivity enhancements.

This impact is particularly pronounced in sectors like banking, technology, retail, and healthcare. The high-tech and banking sectors, in particular, stand to benefit significantly. Generative AI is poised to accelerate software development, revolutionizing these industries with increased efficiency and innovative capabilities. We have observed strong interest from these two sectors in leveraging our code optimization technology to develop high-performance applications, reduce costs, and cut carbon emissions.

Are there any notable applications of generative AI that you find particularly promising or revolutionary for businesses?

Generative AI presents significant opportunities for businesses across various domains, notably in marketing, sales, software engineering, and research and development. According to McKinsey, these areas account for approximately 75% of generative AI’s total annual value.

One of the standout areas of generative AI application is in data-driven decision-making, particularly through the use of Large Language Models (LLMs). LLMs excel in analyzing a wide array of data sources and streamlining regulatory tasks via advanced document analysis. Their ability to process and extract insights from unstructured text data is particularly valuable. In the financial sector, for instance, LLMs enable companies to tap into previously underutilized data sources like news reports, social media content, and publications, opening new avenues for data analysis and insight generation.

The impact of generative AI is also profoundly felt in software engineering, a critical field across all industries. The potential for productivity improvements here is especially notable in sectors like finance and high-tech. An interesting trend in 2023 is the growing adoption of AI coding tools by traditionally conservative buyers in software, such as major banks including Citibank, JPMorgan Chase, and Goldman Sachs. This shift indicates a broader acceptance and integration of AI tools in areas where they can bring about substantial efficiency and innovation.

How can businesses harness the potential of generative AI while addressing potential ethical concerns and biases?

The principles of ethical practice and safety should be at the heart of implementing and using generative AI. Our core ethos is the belief that AI must be secure, reliable, and efficient. This means ensuring that our products, including evoML and Artemis AI, which utilize generative AI, are carefully crafted, maintained, and tested to confirm that they perform as intended.

There is a pressing need for AI systems to be free of bias, including biases present in the real world. Therefore, businesses must ensure their generative AI algorithms are optimized not only for performance but also for fairness and impartiality. Code optimization plays a crucial role in identifying and mitigating biases that might be inherent in the training data and reduces the likelihood of these biases being perpetuated in the AI’s outputs.

More broadly, businesses should adopt AI governance processes that include the continuous assessment of development methods and data and provide rigorous bias mitigation frameworks. They should scrutinize development decisions and document them in detail to ensure rigor and clarity in the decision-making process. This approach enables accountability and answerability.

Finally, this approach should be complemented by transparency and explainability. At TurinTech, for example, we ensure our decisions are transparent company-wide and also provide our users with the source code of the models developed using our platform. This empowers users and everyone involved to confidently use generative AI tools.

The Need for Sustainable AI:

Sustainable AI is becoming increasingly important. What are the environmental and ethical implications of AI development, and why is sustainability crucial in this context?

More than 1.3 million UK businesses are expected to use AI by 2040, and AI itself has a high carbon footprint. A University of Massachusetts Amherst study estimates that training a single Natural Language Processing (NLP) model can generate close to 300,000 kg of carbon emissions.

According to an MIT Technology Review article, this amount is “nearly five times the lifetime emissions of the average American car (and that includes the manufacture of the car itself).” With more companies deploying AI at scale, and in the context of the ongoing energy crisis, the energy efficiency and environmental impact of AI are becoming more crucial than ever before.

Some companies are starting to optimize their existing AI and code repositories using AI-powered code optimization techniques to address energy use and carbon emission concerns before deploying a machine learning model. However, most regional government policies have yet to significantly address the profound environmental impact of AI. Governments around the world need to emphasize the need for sustainable AI practices before it causes further harm to our environment.

Can you share some insights into how businesses can achieve sustainable AI development without compromising on performance and innovation?

Sustainable AI development, where businesses maintain high performance and innovation while minimizing environmental impact, presents a multifaceted challenge. To achieve this balance, businesses can adopt several strategies.

Firstly, AI efficiency is key. By optimizing AI algorithms and code, businesses can reduce the computational power and energy required for AI operations. This not only cuts down on energy consumption and associated carbon emissions but also ensures that AI systems remain high-performing and cost-effective.

In terms of data management, employing strategies like data minimization and efficient data processing can help reduce the environmental impact. By using only the data necessary for specific AI tasks, companies can lower their storage and processing requirements.

Lastly, collaboration and knowledge sharing in the field of sustainable AI can spur innovation and performance. Businesses can find novel ways to develop AI sustainably without compromising on performance or innovation by working together, sharing best practices, and learning from each other.

What are some best practices or frameworks that you recommend for businesses aiming to integrate sustainable AI practices into their strategies?

Creating and adopting energy-efficient AI models is particularly necessary for data centers. While this is often overlooked by data centers, using code optimization means that traditional, energy-intensive software and data processing tasks will consume significantly less power.

I would then recommend using frameworks such as a carbon footprint assessment to monitor current output and implement plans for reducing these levels. Finally, overseeing the lifecycle management of AI systems is crucial, from collecting data and creating models to scaling AI throughout the business.

Final Thoughts:

In your opinion, what key takeaways should business leaders keep in mind when considering the optimization of AI code and the future of AI in their organizations?

When considering the optimization of AI code and its future role in their organizations, business leaders should focus on several key aspects. Firstly, efficient and optimized AI code leads to better performance and effectiveness in AI systems, enhancing overall business operations and decision-making.

Cost-effectiveness is another crucial factor, as optimized code can significantly reduce the need for computational resources. This lowers operational costs, which becomes increasingly important as AI models grow in complexity and data requirements. Moreover, future-proofing an organization’s AI capabilities is essential in the rapidly evolving AI landscape, with code optimization ensuring that AI systems remain efficient and up-to-date.

With increasing regulatory scrutiny on AI practices, optimized code can help ensure compliance with evolving regulations, especially in meeting ESG (Environmental, Social, and Governance) compliance goals. It is a strategic imperative for business leaders, encompassing performance, cost, ethical practices, scalability, sustainability, future-readiness, and regulatory compliance.

As we conclude this interview, could you provide a glimpse into what excites you the most about the intersection of code optimization, AI, and sustainability in business and technology?

Definitely. I’m excited about sustainable innovation, particularly leveraging AI to optimize AI and code. This approach can really accelerate innovation with minimal environmental impact, tackling complex challenges sustainably. Generative AI, especially, can be resource-intensive, leading to a higher carbon footprint. Through code optimization, businesses can make their AI systems more energy-efficient.

Secondly, there’s the aspect of cost-efficient AI. Improved code efficiency and AI processes can lead to significant cost savings, encouraging wider adoption across diverse industries. Furthermore, optimized code runs more efficiently, resulting in faster processing times and more accurate results.

Do you have any final recommendations or advice for businesses looking to leverage AI optimally while remaining ethically and environmentally conscious?

I would say the key aspect to embody is continuous learning and adaptation. It’s vital to stay informed about the latest developments in AI and sustainability. Additionally, fostering a culture of continuous learning and adaptation helps integrate new ethical and environmental standards as they evolve.

Leslie Kanthan

Chief  Executive Officer and Founder at TurinTech AI

Dr Leslie Kanthan is CEO and co-founder of TurinTech, a leading AI Optimisation company that empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle. Before TurinTech, Leslie worked for financial institutions and was frustrated by the manual machine learning developing process and manual code optimising process. He and the team therefore built an end-to-end optimisation platform – EvoML – for building and scaling AI.

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Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. Lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. In the same way, many analytical positions will become redundant as smart chatbots assume more daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. The fast-growing demand for data analysis will remain immune to economic pressures, and those kinds of job positions will be less susceptible to budget cuts.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

AI Marketing Trends Impact Vertical Industry Groups

In addition to affecting job roles, the AI revolution is expected to supercharge marketing functions across nearly every type of industry. Two obvious examples include the retail and healthcare sectors. The retail industry has been quick to integrate AI to deliver efficiencies and increase sales. One emerging innovation is to combine neural networks with a shopper and a product to create new retail marketing experiences. For example, starting in 2024, you can expect an AI assistant to showcase an item of clothing on a model with similar dimensions to see exactly how it will look in various poses. Most industry watchers believe that such immersive, highly personalized virtual experiences will be the future of retail.

AI is also creating a radical new reality for the healthcare industry. For instance, digital twins are becoming increasingly ubiquitous for researchers, physicians, and therapists. A digital twin is a virtual model that accurately replicates a physical object or system. In this way, users can simulate physical processes through digital twins to test various outcomes without involving actual products or people, which greatly reduces operational costs and risks to public safety. For example, AI-powered digital twins could usher in new ways of marketing healthcare services for an aging population, by allowing people to live independently for longer. Or such twins might be used for future drug development projects.

AI will also play a pivotal role in the early diagnosis of potential health issues. For example, full-body MRIs will tap into the ability of AI to identify, analyze, and predict data patterns to help diagnose diseases long before any symptoms are visible to the human eye. In addition, AI will take a more prominent role in assisting medical staff to understand and interpret findings and provide treatments and care recommendations. All of these AI benefits will help sales and marketing teams to craft new messages that can communicate such considerable advantages to consumers.

Artificial intelligence engines have already upended marketing practices based on their extraordinary capacity for data analysis and efficiency, and this growth trend is only expected to continue in the coming years. To keep up with these technical developments, marketing professionals should become more comfortable using the AI tools which are rapidly remaking the entire marketing landscape.

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With 2024 well underway, we are already witnessing how generative artificial intelligence (GenAI) is propelling the cybersecurity arms race among organizations. As both defensive and offensive players adopt and operationalize finely-tuned Large Language Models (LLMs) and Mixture of Experts (MoE) model-augmented tools, the approach organizations take toward cybersecurity must evolve rapidly. For instance, GenAI-powered capabilities such as automated code generation, reverse engineering, deepfake-enhanced phishing, and social engineering are reaching levels of sophistication and speed previously unimaginable.

The urgency to rapidly adopt and deploy these AI-augmented cybersecurity tools is mounting, and organizations that are reluctant to invest in and adopt these tools will inevitably fall behind, placing themselves at a significantly higher risk of compromise. While it is imperative for organizations to move swiftly to keep pace with this rapid advancement, it is equally crucial to acknowledge the intricate nature of GenAI and its potential to be a double-edged sword. To avoid the perils of AI and leverage its benefits, organizations must comprehend the importance of keeping abreast of its advancements, recognize the dual capacity for good and harm inherent in this technology, and implement internal processes to bridge knowledge gaps and tackle AI-related risks. To counteract known and emerging AI-related threats, such as data leakage, model poisoning, bias, and model hallucinations, it is essential to establish additional security controls and guardrails before operationalizing these AI technologies.

Keeping pace with adversaries

The challenge posed by AI-powered security threats lies in their rapid evolution and adaptability, which can render conventional signature and pattern-based detection methods ineffective. To counter these AI-based threats, organizations will need to implement AI-powered countermeasures. The future of cybersecurity may well be characterized by a cyber AI arms race, where both offensive and defensive forces leverage AI against one another.

It is widely recognized that cyber attackers are increasingly using GenAI tools and LLMs to conduct complex cyber-attacks at a speed and scale previously unseen. Organizations that delay the implementation of AI-driven cyber defense solutions will find themselves at a significant disadvantage. They will struggle not only to adequately protect their systems against AI-powered cyberattacks but also inadvertently position themselves as prime targets, as attackers may perceive their non-AI-protected systems as extremely vulnerable.

Advantages versus potential pitfalls

When appropriately implemented, safeguarded, and utilized, technologies like GenAI have the potential to significantly enhance an organization’s cyber defense capabilities. For instance, foundational and fine-tuned (LLMs) can expedite the processes of cyber threat detection, analysis, and response, thus enabling more effective decision-making and threat neutralization. Unlike humans, LLM-augmented systems can quickly identify new patterns and subtle correlations within extensive datasets. By aiding in the swift detection, containment, and response to threats, LLMs can alleviate the burden on cybersecurity analysts and diminish the likelihood of human error. Additional benefits include an increase in operational efficiency and a potential reduction in costs.

There is no doubt that technologies such as GenAI can provide tremendous benefits when used properly. However, it is also important not to overlook the associated risks. For instance, GenAI-based systems, especially LLMs, are trained on very large datasets from various sources. To mitigate risks such as data tampering, model bias, or drift, organizations need to establish rigorous processes to address data quality, security, integrity, and governance. Furthermore, the resulting models must be securely implemented, optimized, and maintained to remain relevant, and their usage should be closely monitored to ensure ethical use. From a cybersecurity perspective, the additional compute and data storage infrastructure and services needed to develop, train, and deploy these AI models represent an additional cyber-attack vector. To best protect these AI systems and services from internal or external threat actors, a comprehensive Zero Trust Security-based approach should be applied.

Adopting AI for Cybersecurity Success

Considering the breakneck speed at which AI is being applied across the technology and cybersecurity landscape, organizations may feel compelled to implement GenAI solutions without an adequate understanding of the investments in time, labor, and expertise required across data and security functions.

It may seem counterintuitive, but a sound strategy for incorporating artificial intelligence (which, on its face, would seem to offset the need for human efforts) involves no small amount of human input and intellect. As they adopt these new tools, CTOs and tech leadership will need to consider:

• AI advancement – It is an absolute certainty that GenAI will continue to be a fluid, constantly evolving tool. Engineers and technicians will need to stay abreast of its shifting offensive and defensive capabilities.
• Training and upskilling – Because AI will never be a static technology, organizations must support ongoing learning and skills development for those closest to critical AI and cybersecurity systems.
• Data quality and security – Artificial intelligence deployed for cybersecurity is only as good as the data that enables its learning and operation. Organizations will require a robust operation supporting the secure storage, processing, and delivery of data-feeding AI.

Undoubtedly, leaders are feeling the urgency to deploy AI, particularly in an environment where bad actors are already exploiting the technology. However, a thoughtful, strategic approach to incorporating artificial intelligence into cybersecurity operations can be the scaffolding for a solid program that greatly mitigates vulnerabilities and protects information systems far into the future.

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The intersection of artificial intelligence (AI) and digital advertising to create truly engaging experiences across global audiences and cultures is reaching an inflection point. Companies everywhere are leveraging powerful trends in AI, machine learning and apps for performance marketing.

Today’s AI and machine learning technologies are allowing apps to understand speech, images, and user behavior more naturally. As a result, apps with AI capabilities are smarter and more helpful, and companies are using these technologies to create tailored experiences for customers, regardless of language or background. AI is leveling the playing field by making advanced data tools accessible to anyone, not just data scientists.

Kochava has incorporated AI and machine learning across our diverse solutions portfolio for years, such as within our advanced attribution and fraud prevention products. We have also adopted advanced technologies, like large language models (LLMs) to develop new tools.

Many organizations are instituting internal restructuring with a focus on enhancing the developer experience. The aim is to leverage the full potential of AI for smart applications, providing universal access to advanced tech tools, while adapting to changes in app store policies. Engineering teams are spearheading the development of self-service platforms managed by product teams. The primary objective is to optimize developers’ workflows, speeding up the delivery of business value, and reducing stress. These changes improve the developer experience which can help companies retain top talent.

From an overall organizational structure perspective, in pursuit of a more efficient and effective approach, Kochava is focused on enhancing developer experiences, leveraging AI for intelligent applications, democratizing access to advanced technologies, and adapting to regulatory changes in app marketplaces.

Reimagining the Future

The software and applications industry is one that evolves particularly quickly. The app market now represents a multibillion-dollar sector exhibiting no signs of slowing. This rapid growth and constant change presents abundant opportunities for developers to build innovative new applications while pursuing their passions. For app developers, monitoring trends provides inspiration for maintaining engaging, innovative user experiences.

As AI integration increases, standards will develop to ensure AI can automatically interface between applications. It will utilize transactional and external data to provide insights. Applications will shift from set features to AI-driven predictions and recommendations tailored for each user. This advances data-driven decision making and transforms the experience for customers, users, teams, and developers.

Democratizing access to generative AI across organizations has the potential to automate many tasks, lower costs and create new opportunities. It changes the competitive landscape by making vast knowledge more accessible to anyone through natural language. Increased access to information is a big trend.

Changes in regulations are also allowing third-party app stores on many operating systems. This reflects a shift that provides developers new opportunities and challenges as larger publishers set up their own markets.

Trends Transforming How We Work

Intelligent Applications: With increasing AI integration, standards will develop to ensure AI can automatically interface with other applications. It utilizes transactional and external data to provide application insights. Shifting from procedural features to AI-driven predictions and recommendations personalized for users advances data-driven decision-making, transforming customer, user, product owner, architect, and developer experiences.

Generative AI Democratization: Democratizing access to generative AI across the organization. With broad task automation potential, it reduces costs while fostering growth opportunities. This transforms enterprise competitiveness and facilitates democratized information and skills access across roles and functions. Enabling natural language interface access to vast knowledge equitably is key.

Third-Party App Stores: Regulation changes may allow third-party app stores on operating systems, anticipated to increase larger publishers’ establishment in these markets. This reflects an evolving app store ecosystem with new opportunities and challenges for both platform operators and developers.

AI can help marketers think outside the box to find those gems that standard sources aren’t turning up. Exercise AI’s power to maximize your marketing campaigns and connect with customers worldwide!

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Patrick, please guide us through your journey from co-founding Terakeet in 2001 to guiding its growth for 23 years. How has that shaped your perspective on owned asset optimization and customer connections?

Since the beginning, a constant for Terakeet has been a willingness to evolve. My business partner Mac Cummings and I started the company in 2001, the start of a very tumultuous, exciting, and innovative time in not just the marketing and technology spaces, but the entire world. Because of that, our team has had to anticipate and respond to market shifts within our ever-evolving industry.

Our willingness to humble ourselves and learn has played a significant role in Terakeet’s success. In 2023, we recognized an imminent shift in consumer needs and took charge of the industry’s evolution. This is how owned asset optimization (OAO) originated. Instead of pushing today’s empowered consumer through an arbitrary funnel, we’d use their behavior data to understand what they truly want, then provide content resources to support them, and truly connect.

Over two decades of guiding the company’s product vision, what keeps you motivated to stay at the forefront of technological platforms?

My team is incredibly smart — being around them, and having real trust in their ideas is a powerful thing for me. Together, we solve complex problems by rolling up our sleeves and figuring out new approaches, which can be very fulfilling. Doing this work on behalf of global clients and innovating in the marketing industry, like we’ve had the opportunity to do at Terakeet, adds to that fulfillment even more. I’d say my team’s refusal to take an inefficient status quo as the final state still propels us toward innovation 23 years later.

Terakeet serves as the preferred OAO partner for Fortune 500 brands. How does your unique approach to leveraging owned assets differentiate Terakeet in the market?

Our OAO offering builds on our decades of experience in enterprise SEO, our deep bench of experts, and our proprietary data-driven understanding of consumer intent and preferences. 

Our strategies help brands optimize their digital experiences and establish themselves as the best, most trusted solution to unmet needs throughout the consumer journey. This enhances customer satisfaction and fortifies brand reputation. Our approach prioritizes owned assets, the digital properties brands control, as the path towards impressive cross-channel results including reduced Cost Per Acquisition (CPA) and significantly improved ROI across the board.

How does the company stay ahead in terms of innovation to continuously offer a pioneering approach in the industry?
Rather than respond to change, Terakeet has become comfortable with charting a new course for an entire industry. I believe this can be attributed to two things. First,  our team of experts and their unrelenting pursuit of improvement and innovation keeps us on the cutting-edge. Second, our proprietary technology provides us with real-time, accurate consumer behavior and market insights. This data gives us a unique advantage because we can keep a pulse on consumer connection opportunities and build or adjust impactful strategies to meet them.

You emphasize using automation as a strategy, not just a solution. How does this approach allow brands to uncover insights faster and drive more value for customers?

In its current state, using AI to generate complete content presents risks that outweigh the efficiency gains. Current LLM-driven tools, such as OpenAI’s ChatGPT, while useful, do not inherently optimize content for marketing purposes, such as user experience, conversion, and search visibility. At times, AI-generated content can be nonsensical, repetitive, unethical, contradictory, and, often worst, overly homogeneous due to its public training on low quality, unoriginal, practices. 

Marketers must view AI as a helpful and collaborative tool rather than a marketing panacea. A strong AI strategy must combine robust methodologies, substantial content resources, proprietary data, and brand-oriented models. This will allow marketers to rapidly reveal insights, provide more value to customers, and generate data-driven content at scale, while avoiding poor user experience and market sameness for consumers.

Brands are advised to inject their essence and proprietary data into AI strategies. How does Terakeet assist brands in maintaining their personality and uniqueness in a crowded market?

Like I mentioned previously, we see AI as a tool for our team to use in support of our customers, not as a cure-all that ultimately just adds to market noise. That’s why our LLMs are meticulously trained on decades of proven patterns and outputs to build a differentiated use of AI, then paired with unique brand values and real-time consumer data. This helps our team create, at scale, custom, impactful digital strategies for customers that are fresh, diverse, and speak to consumer needs.

How does the balance between automation and creativity contribute to content that speaks directly to audience needs?

Consumer data should be a compass for brands’ marketing decisions. Understanding what consumers are looking for and when they’re searching should be every marketer’s goal. However, good consumer data is often discovered in a complicated deluge of disparate information. Automation can make the data less opaque and deliver insights that are more actionable. 

AI assistance’s ability to digest and translate massive amounts of data, like social media engagement, website interactions, consumer sentiment, and emerging industry trends, allows content marketers to calibrate their narratives toward consumer interests. It also bolsters disparate strategies to be as effective as possible. 

How does Terakeet guide brands in effectively prioritizing their owned assets to enhance customer connection and build brand affinity?
We believe that building trust with consumers is the best way to ensure future business growth. And we believe that the most effective way to build that trust is to provide the best answers to consumer questions, across their entire journey. 

To do this, our team uses unbiased, proprietary consumer behavior data to learn what a brand’s consumers are looking for in real time. Then, we review the ways the brand is being perceived, and how that differs from their stated goals. Using that consumer and market insight, our experts in content strategy, UX, and analytics identify consumer pain points and build content strategies that connect the brand with the right audience. This could involve updating existing owned assets or creating new ones, all customized to the specific goals, market, and brand of each client, all connected to a cohesive brand message and all optimized for visibility. This allows the brand to build long-term relationships with consumers based on trust and convert more effectively.

Are there specific areas of technological advancement or industry trends that Terakeet is particularly excited about incorporating in its future strategies?
We’re excited to continue working to optimize content for emerging channels. For example, as generative AI search continues to evolve and become a more incorporated aspect of user behavior, we’re ready, and have already begun, to use it as an effective tool for brands to reach consumers with preferred content.

In the end, what is your advice to young entrepreneurs who are trying to make their name in the technology industry?

Don’t try to do it all at once. It’s a matter of quality over quantity – spreading resources and efforts across multiple objectives often leads to diminishing returns. I’ve found, through my own trial and error, that strategic alignment around a limited set of clear, well-defined goals is most beneficial for organizational success.

Patrick Danial

CTO & Co-Founder at Terakeet

Patrick Danial is the Chief Technology Officer and Co-Founder of Terakeet, the preferred owned asset optimization (OAO) partner for Fortune 500 brands seeking meaningful customer connections and online business growth. He has built, operated, and led several industry-leading technological platforms and guided Terakeet’s vision and innovation for 23 years.

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Table of contents

Introduction
1. Reshaping Customer Engagement With Large Language Models
2. Importance of Knowledge Graphs for Complex Data
3. Embrace Web 3.0 For a Decentralized Future
4. Revolutionizing Business Operations With Virtual Assistants
Conclusion

Introduction

As we have stepped into the realm of 2024, the artificial intelligence and data landscape is growing up for further transformation, which will drive technological advancements and marketing trends and understand enterprises’ needs. The introduction of ChatGPT in 2022 has produced different types of primary and secondary effects on semantic technology, which is helping IT organizations understand the language and its underlying structure. 

For instance, the semantic web and natural language processing (NLP) are both forms of semantic technology, as each has different supportive rules in the data management process.

In this article, we will focus on the top four trends of 2024 that will change the IT landscape in the coming years.

1. Reshaping Customer Engagement With Large Language Models

The interest in large language models (LLMs) technology came to light after the release of ChatGPT in 2022. The current stage of LLMs is marked by the ability to understand and generate human-like text across different subjects and applications. The models are built by using advanced deep-learning (DL) techniques and a vast amount of trained data to provide better customer engagement, operational efficiency, and resource management. 

However, it is important to acknowledge that while these LLM models have a lot of unprecedented potential, ethical considerations such as data privacy and data bias must be addressed proactively.

2. Importance of Knowledge Graphs for Complex Data

The introduction of knowledge graphs (KGs) has become increasingly essential for managing complex data sets as they understand the relationship between different types of information and segregate it accordingly. The merging of LLMs and KGs will improve the abilities and understanding of artificial intelligence (AI) systems. This combination will help in preparing structured presentations that can be used to build more context-aware AI systems, eventually revolutionizing the way we interact with computers and access important information.

As KGs become increasingly digital, IT professionals must address the issues of security and compliance by implementing global data protection regulations and robust security strategies to eliminate the concerns.  

3. Embrace Web 3.0 For a Decentralized Future

The core of Web 3.0 (Web3) lies in three fundamentals: smart contracts, blockchain, and digital assets, which allow platform users to manage their authority and enable them to participate in the growth of the technological ecosystem. Web3 allows IT professionals to create, own, and manage their content and enjoy authority over their assets and data to safeguard them, as this technology eliminates third-party control and enhances the user’s experience by focusing on privacy, transparency, and security.

4. Revolutionizing Business Operations With Virtual Assistants

The concept of virtual assistance (VA) has transformed the way we work, interact, and communicate with technology, as it offers a myriad of benefits such as maximizing productivity, streamlining daily operations, and offering personalized customer experiences. The introduction of technological advancements such as AI, NLP, and DL has improved the VA’s intelligence and capabilities, allowing the VA application to address specific news and solve real-world issues. 

Conclusion 

The introduction of LLMs and semantic technologies has had a major impact on the AI-driven world, especially after the successful launch of ChatGPT, which has changed the way of communicating and also solved the problem of language translation.

However, with time, LLMs will be promoted as AI revolutionizes, and knowledge graphs will become more useful and recognizable platforms for data professionals. We also anticipate improvements in search engines and research, with Web3 taking the driver’s seat in changing the internet. 

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Background

To start, Daniel, could you please provide a brief introduction to yourself and your work at Kognic?

 I’m an experienced machine-learning expert and passionate about making AI useful for safety critical applications. As CEO and Co-Founder of Kognic, I lead a team of data scientists, developers and industry experts. The Kognic Platform empowers industries from autonomous vehicles to robotics – Embodied AI as it is called – to accelerate their AI product development and ensure AI systems are trusted and safe. 

Prior to founding Kognic, I worked as a Team Lead for Collection & Analysis at Recorded Future, gaining extensive experience in delivering machine learning solutions at a global scale and I’m also a visiting scholar at both MIT and UC Berkeley.

Overview

Can you give our audience an overview of what AI alignment is and why it’s important in the context of artificial intelligence?

AI alignment is a new scope of work that aims to ensure that AI systems achieve their desired outcomes and work properly for humans. It aims to create a set of rules to which an AI-based system can refer when making decisions and to align those decisions with human preferences. 

Imagine playing darts, or any game for that matter, but not agreeing on what the board looks like or what you get points for?  If the product developer of an AI system cannot express consistent and clear expectations through feedback, the system won’t know what to learn. Alignment is about agreeing on those expectations.

AI Alignment and Embodied AI

How does ensuring AI alignment contribute to the safe and ethical development of Embodied AI?

A significant amount of the conversation around AI alignment has been on its ability to mitigate the development of a ‘God-like’ super powered AI that would no longer work for humans and potentially pose an existential threat. Whilst undoubtedly an important issue, such focus on AI doomsday predictions doesn’t reflect the likelihood of such an eventuality, especially in the short or medium term.

However, we have already seen how the misalignment of expectations between humans and AI systems has caused issues over the past year, from LLMs such as ChatGPT generating false references and citations to its ability to generate huge amounts of misinformation. As Embodied AI becomes more common – where AI is embedded in physical devices, such as Autonomous Vehicles – AI Alignment will become even more integral to ensure the safe and ethical development of AI systems over the coming years.

Could you share any real-world examples or scenarios where AI alignment played a critical role in decision-making or Embodied AI system behaviour?

One great example within the automotive industry and the development of autonomous vehicles, starts with a simple question: ‘what is a road?’

The answer can actually vary significantly, depending on where you are in the world, the topography of the area you are in and what kind of driving habits you lean towards. For these factors and much more, aligning and agreeing on what is a road is far easier said than done. 

So then, how can an AI product or autonomous vehicle make not only the correct decision but one that aligns with human expectations? To solve this, our platform allows for human feedback to be efficiently captured and used to train the dataset used by the AI model.

Doing so is no easy task, there’s huge amounts of complex data an autonomous vehicle is dealing with, from multi-sensor inputs from a camera, LiDAR, and radar data in large-scale sequences, highlighting not only the importance of alignment but the challenge it poses when dealing with data.

Teaching machines

Teaching machines to align with human values and intentions is known to be a complex task. What are some of the key techniques or methodologies you employ at Kognic to tackle this challenge?

Two key areas of focus for us are machine accelerated human feedback and the refinement and fine-tuning of data sets.

First, without human feedback we cannot align AI systems, our dataset management platform and its core annotation engine make it easy and fast for users to express opinions about this data while also enabling easy definition of expectations. 

The second key challenge is making sense of the vast swathes of data we require to train AI systems. Our dataset refinement tools help AI product teams to surface both frequent and rare things in their datasets. The best way to make rapid progress in steering an AI product is to focus on that which impacts model performance. In fact, most teams find tons of frames in their dataset that they hadn’t expected with objects they don’t need to worry about – blurry images at distances that do not impact the model. Fine-tuning is essential to gaining leverage on model performance.  

In your opinion, what role does reinforcement learning play in training AI systems to align with human goals, and what are the associated difficulties?

Reinforcement Learning from Human Feedback (RLHF) uses methods to directly optimise a language model with human feedback. While RLHF has enabled large language models (LLMs) to help align previously trained output, those models work from a general corpus of text. In Embodied AI, such as Autonomous Driving, the dataset is far more complex. This includes video, camera, radar, LiDAR plots of varying sequence and time, other vehicle sensor data such as temperature, motion, pressure…. Human feedback in this context can be reinforced, but automation will only get you so far. 

The iterative mindset

Daniel, you have mentioned the importance of an iterative mindset in AI. Could you explain what you mean by this and why it’s crucial to the development of AI systems?

We believe that good things come to those who iterate. We live in a fast changing world and data sets used to train and align AI systems have to reflect that. AI systems and products are generating and collecting huge amounts of data and given the reality that data doesn’t sleep, there is a need for both flexibility and scale when optimising. Our tools are designed with this in mind, making it easy to change decisions based on new learnings and to do so at a lower cost. 

Many businesses aren’t comfortable working in this manner. The automotive sector for instance has traditionally operated on a waterfall methodology, but it is absolutely vital we see a mindset shift if we are to successfully align AI systems with human expectations. 

Can you share any specific strategies or best practices for implementing an iterative mindset in AI alignment projects?

One strategy is to remap software organisations inside enterprises to think about “programming with data” versus “programming with code”. For this, the skill sets of product developers, engineers and other technical staff need to be adept and comfortable with exploring, shaping and explaining their datasets. Stop trying to address machine learning as a finite process, but rather an ongoing cycle of annotation, insights and refinement against performance criteria., 

Final thoughts

To wrap up, what advice would you give to organisations or researchers who are actively working on AI alignment and ethics in artificial intelligence? What steps can they take to make progress in this field?

Our team at Kognic is bullish on AI and the promise that it can improve our world. For this, the biggest advantage to exploit is a mindset that consistently challenges the immediate way of working – particularly when results from an AI product are not what you expect. We are telling the “machine” what to aspire to, what to aim for… And that job doesn’t end. Keep at it and improvements will cascade into more efficient and safer AI experiences.

Daniel Langkilde

CEO and Co-founder of Kognic

Daniel Langkilde, CEO and co-founder of Kognic, is an experienced machine-learning expert and leads a team of data scientists, developers and industry experts that provide the leading data platform that empowers the global automotive industry’s Automated Driver Assist and Autonomous Driver systems known as ADAS/AD. Daniel is passionate about the challenge of making machine-learning useful for safety-critical applications such as autonomous driving.

Prior to founding Kognic, Daniel was Team Lead for Collection & Analysis at Recorded Future, the world’s largest intelligence company maintaining the widest holdings of interlinked threat data sets. At Recorded Future, Daniel got extensive experience in delivering machine learning solutions at global scale.

Daniel holds a M.Sc. in Engineering Mathematics and has been a Visiting Scholar at both MIT and UC Berkeley.

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